Introduction to Artificial Intelligence: Genetic Algorithms for Sudoku - Lecture 12

Explore a comprehensive lecture on artificial intelligence focusing on genetic algorithms applied to Sudoku puzzles. Learn about assignment goals, Sudoku mechanics, user interface design, and key genetic algorithm concepts including fitness functions, population size, mutation rates, and elitism. Dive into the implementation details with explanations of the Sudoku class, GASettings class, and GA_Student class. Understand the GAEvolve function, selection methods like roulette wheel, crossover techniques, and mutation strategies. Gain insights into modifying fitness functions for Sudoku and generating random populations. This lecture, part of the COMP3200 Intro to Artificial Intelligence course at Memorial University, provides a thorough foundation for implementing genetic algorithms to solve complex puzzles.

- Preroll
- Greetings
- A4 Intro
- Assignment Goals
- Sudoku Tutorial
- User Interface
- Fitness Functions
- Population Size
- Mutation Rate
- Random Gene Rate
- Elitism Rate
- Algorithm Overview
- Assignment Files
- Sudoku Class
- GASettings Class
- Sample Fitness Functions
- GA_Student Class Overview
- Population Individual Object Variables
- GAEvolve Function
- Roulette Wheel Selection
- Child Recombination / Crossover
- Mutate Individual
- Sudoku Fitness Modification
- Assignment Marking Scheme
- Generating Random Population